A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Modeling Right-Turn Blockage and Approach Capacity at Signalized Intersections with Channelized Right-Turn Lanes
Right-turn channelization is used to improve the capacity at busy intersections with a lot of right-turns. However, under heavy traffic conditions the through lane vehicles might backup and block the channelization lane. This will affect the discharge rate of right-turning vehicles and reduce the approach capacity. If the right-turn channelization gets blocked frequently, its advantage is neglected and serious capacity problems can be overlooked. This issue is not addressed in the Highway Capacity Manual (HCM), and no separate model is provided to estimate the capacity of approaches with channelized right-turn lanes. Using conventional methods for estimating the capacity without considering the effect of potential blockage would result in overestimation of the approach capacity. This paper presents a probabilistic capacity model for signalized intersections with channelized right-turn lanes considering the possibility of the right-turn vehicles being blocked from accessing the channelization lane. The capacity model was developed as a function of the short-lane section length and the proportion of right-turn traffic by taking into account the probability of blockage. Subsequently, a model was developed to estimate the probability of blockage with respect to the possible residual queue. The proposed capacity model and the blockage probability model were validated on the basis of VISSIM simulations. For operational purposes, the recommended lengths of the short-lane section were developed to prevent blockage at a certain threshold. The recommended lengths would be useful in evaluating adequacy of the current lengths, identifying the options of extending the short-lane section length, or changing signal timing to reduce the likelihood of blockage.
Modeling Right-Turn Blockage and Approach Capacity at Signalized Intersections with Channelized Right-Turn Lanes
Right-turn channelization is used to improve the capacity at busy intersections with a lot of right-turns. However, under heavy traffic conditions the through lane vehicles might backup and block the channelization lane. This will affect the discharge rate of right-turning vehicles and reduce the approach capacity. If the right-turn channelization gets blocked frequently, its advantage is neglected and serious capacity problems can be overlooked. This issue is not addressed in the Highway Capacity Manual (HCM), and no separate model is provided to estimate the capacity of approaches with channelized right-turn lanes. Using conventional methods for estimating the capacity without considering the effect of potential blockage would result in overestimation of the approach capacity. This paper presents a probabilistic capacity model for signalized intersections with channelized right-turn lanes considering the possibility of the right-turn vehicles being blocked from accessing the channelization lane. The capacity model was developed as a function of the short-lane section length and the proportion of right-turn traffic by taking into account the probability of blockage. Subsequently, a model was developed to estimate the probability of blockage with respect to the possible residual queue. The proposed capacity model and the blockage probability model were validated on the basis of VISSIM simulations. For operational purposes, the recommended lengths of the short-lane section were developed to prevent blockage at a certain threshold. The recommended lengths would be useful in evaluating adequacy of the current lengths, identifying the options of extending the short-lane section length, or changing signal timing to reduce the likelihood of blockage.
Modeling Right-Turn Blockage and Approach Capacity at Signalized Intersections with Channelized Right-Turn Lanes
Farivar, Saeedeh (author) / Andalibian, Rasool (author) / Tian, Zong Z. (author)
2016-11-18
Article (Journal)
Electronic Resource
Unknown
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